Guideline-Recommended Medications: Variation Across Medicare Advantage Plans and Associated Mortality

OBJECTIVES: To evaluate variation in the prescription of guideline-recommended medications across Medicare Advantage (MA) plans and to determine whether such variation is associated with increased mortality. METHODS: Observational study of 111,667 patients aged 65 years or older receiving care in 203 MA plans. We linked data from the Medicare Health Outcomes (HOS) Survey cohort 9 (April 2006–May 2008) with the Medicare Part D prescription benefit files (January 1, 2006–December 31, 2007) to examine variation in treatment across MA plans and its association with differences in observed (O)/expected (E) mortality ratio for 5 high-volume chronic conditions: diabetes, coronary artery disease (CAD), congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD)/asthma, and depression. RESULTS: Analysis of variance confirmed that the 203 MA plans differed significantly in their use of guideline-recommended treatment (P≤0.02). Those MA plans with higher use of angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers (r=-0.40; P  less than  0.0001) and beta-blockers (r=-0.27; P  less than  0.0001) in patients with CHF were significantly associated with lower O/E mortality ratios. Those MA plans with higher use of multiple guideline-recommended medications were significantly associated with lower O/E mortality ratios in CHF (r=-0.45; P  less than  0.0001) and diabetes (r=-0.14; P  less than  0.042). There were no significant associations between the variation in performance indicators and mortality ratios in patients with CAD and COPD/asthma. Those MA plans with higher use of antidepressant medications had significantly higher O/E mortality ratios (r=0.28, P  less than  0.0001). CONCLUSIONS: There was wide variation across MA plans in the prescription of guideline-recommended medications that had a measurable relationship to the mortality of elderly patients with CHF and diabetes. These findings can serve to both motivate and target quality improvement programs.

data as well as adjustments for contextual issues such as mode of administration (phone versus mail-out). 10 We used the PCS and MCS to calculate expected mortality rates as described below. 3. Were members of a health plan at baseline that remained in the HOS at follow-up. We identified 188,515 beneficiaries ( Figure 1). Among these 188,515 patients, 111,667 met all 3 of the above study criteria. Of the latter, we identified 94,630 who had Medicare Part D claims. Out of these, 7,148 (7.54%) had died within 2 years of follow-up.

Study Measures
We used 2 study measures: 1.
2-year mortality rates: Building upon prior work, we used risk-adjusted mortality as the study outcome. 11 Mortality is a measure that is particularly relevant to elderly patients and might reflect potentially poor quality of care. 12 We used the Medicare HOS mortality files to ascertain the vital status of the MA patients. These files were created using information from the National Center for Health Statistics mortality files that include a record for every death of a U.S. resident recorded in the United States. 2. Performance indicators: To evaluate performance, we assessed how well plans followed clinical practice guidelines that are nationally recognized, based on scientific evidence and expert consensus, relate to common medical conditions, and address activities that clinicians can control directly. 13 We selected 5 high-volume diagnoses in an ambulatory care setting: diabetes, CAD, CHF, COPD/ asthma and depression (Table 1). 14 19 Within each of the 5 diagnoses, there were 1 to 3 drug interventions that were measured as performance indicators. There were 2 performance indicators for the diagnosis of diabetes: the percentage of patients with diabetes treated with (1) ACE inhibitors or angiotensin II receptor blockers (ARBs) and (2) lipid-lowering medications. There were 2 indicators for the diagnosis of CAD: the percentage of patients with CAD treated with (1) beta-blockers and (2) lipid-lowering medications. There were 3 indicators for the diagnosis of CHF: the percentage of patients with CHF treated with (1) ACE inhibitors or ARBs, (2) beta-blockers, and (3) lipid-lowering medications. There was 1 indicator each for the diagnoses of COPD/asthma and depression: the percentage of patients with COPD/asthma treated with steroid inhalers and the percentage of patients with depression treated with antidepressant medications. We used the Medicare Part D prescription benefit file to calculate the performance indicators. This file contains information that was collected between January 1, 2006, and December 31, 2007. We did not use National Drug Codes (NDCs) from the U.S. Food and Drug Administration because this classification scheme has not been updated since 1976. 20 To identify the medications for each of the selected class groups, we used the Tarascon Pocket Pharmacopoeia 2009 Classic Shirt-Pocket Edition 23rd edition and followed an algorithm similar to the Healthcare Effectiveness Data and Information Set (HEDIS) 2008 NDC class assignment. 21

Analytic Plan
Our first objective was to profile the MA plans based on the proportion of patients treated according to clinical practice guidelines. For each selected performance indicator, we calculated the prevalence for each MA plan. The numerator was the number of patients receiving the desired drug in each MA plan. The denominator had the chronic condition for which the performance indicator applies. For conditions with more than 1 performance indicator such as diabetes, CAD, and CHF, we calculated the proportion of patients (e.g., diabetics) receiving none of the desired medications, only 1 of the desired medications, and 2 or more of the desired medications in each MA plan. We used analysis of variance to examine the variation of the performance indicators across MA plans.
Our second objective was to examine the association between variation of the performance indicators across MA plans and differences in "observed-to-expected" (O/E) mortality ratios. We hypothesized that those plans with the highest use of the desired drugs would have the lowest O/E mortality ratios. We performed a simple linear correlation (Pearson product moment (r) correlations) of the O/E mortality ratios for each of the performance indicators at the plan level. We calculated the ratio between the observed number of deaths for each MA plan and the number of deaths that would be expected. We used the following steps to calculate the patients' expected mortality for each MA plan: Step 1: We computed propensity scores that were defined in 2 ways: (a) as the probability of a patient being on a specific drug for the analysis of individual performance indicators 22 and (b) as the probability of a patient being on any of the eligible drugs for the analysis of multiple performance indicators. Propensity matching was employed in order to control for confounding by indication of medications and to limit as much as possible the problem of endogeneity of medications prescribed by balancing groups using covariates in the propensity score model. We used logistic regression models in which the log of the "odds" of the event was modeled as a linear function of the predictor variables. We included the following predictor variables: age, gender (male/female), race/ethnicity (whites/African Americans/Hispanics/others), married (yes/no), less than high school education (yes/no), homeowner (yes/no), body mass index, baseline physical functioning, baseline PCS and MCS, Activities of Daily Living questionnaire (self-care, household care, employment and recreation, shopping and money, travel, and communication), presence of medical/psychiatric conditions (diabetes, hypertension, stroke, CHF, CAD, COPD/ asthma, cancer, arthritis of the hip, arthritis of the hand, low back pain, osteoporosis, sciatica, depression), shortness of breath, current smoker (yes/no).
Step 2: We generated weights from the propensity scores in step 1 by applying the Horwitz and Thompson weighting approach. 23 This methodology provides unbiased estimates of the probability of being on treatment by comparing treatment groups within each of the stratified levels of the propensity score. The weights were defined as the total number of patients on a drug/10)/number of patients on a drug in 1 decile and the total number of patients not on a drug/10)/number of patients not on a drug. Each individual was assigned with 1 of the 2 weights in each decile.
Step 3: We calculated the expected mortality rate by applying the weights from step 2 to a probability-weighted regression model. In the model, mortality was the dependent variable and medication vs. no medication was the independent variable. The patients' expected mortality rates for each plan were aggregated in order to calculate the plan-expected mortality rate.
In this correlation analysis, a single patient with diabetes could have as many as 2 separate correlations with mortality, 1 for each guideline, when we calculated the prevalence of the selected performance indicators for each MA plan. For example, to calculate the association between ACE/ARB and mortality, we examined all patients with diabetes who qualified for ACE/ARB. Then we repeated the analysis for all patients who qualified for lipid-lowering medications. Thus, some patients were counted twice. We also examined the association between mortality and the multiple performance indicators for www.amcp.org Vol. 19 conditions such as diabetes, CAD, and CHF. We calculated composite scores of the performance indicators as the summation of the different binary performance scores at the patient level and averaged them together for each MA plan. Table 2 summarizes the characteristics of the 94,630 MA patients with Medicare Part D claims. When compared with MA patients without claims (N = 17,037), they were more likely to be female, nonwhite, not married, have less than a high school education, and have an income less than $20,000. The MA patients with Medicare Part D claims also had higher prevalence of chronic conditions, including diabetes (23.0% vs. 18.1%; P < 0.0001), hypertension (65.9% vs. 55.1%; P < 0.0001), CAD (15.7% vs. 14.6%; P = 0.0003), CHF (9.3% vs. 7.9%; P < 0.0001), COPD/asthma (14.4% vs. 11.8%; P < 0.0001), and depression (28.6% vs. 23.0%; P < 0.0001). They had lower PCS scores (39.0 [SD ± 12] vs. 40.8 [SD ± 11]; P < 0.0001) and MCS scores (51.7 [SD ± 11] vs. 53.1 [SD ± 10]; P < 0.0001). There was no difference in the 2-year mortality rates between patients with or without Medicare Part D claims (7.55% vs. 7.54%, respectively). Table 3 shows the variation across MA plans of the percentage of guideline-eligible patients who received an indicated drug. The performance indicator estimates for diabetes ranged from 53.3% to 100% for ACE inhibitors/ARBs and 33.3% to 90.0% for lipid-lowering medications. Estimates for CAD ranged from 42.4% to 100% for beta-blockers and 51.8% to 100% for lipid-lowering medications. Those for CHF ranged from 38.5% to 86.7% for ACE inhibitors/ARBs, from 33.3% to 100% for lipid-lowering medications, and from 46.7% to 93.3% for betablockers. The proportion of patients with diabetes receiving none, 1, or 2 recommended medications ranged from 3.3% to 33.3%, 13.1% to 46.6%, and 33.3% to 85.7%, respectively. The proportion of CAD patients receiving none, 1, or 2 recommended medications ranged from 0% to 33.3%, 0% to 50.0%, and 29.8 to 100%, respectively. The proportion of CHF patients receiving none, 1, 2, or 3 recommended medications ranged from 0% to 28.5%, 0% to 100%, 11.5% to 57.5%, and 14.2% to 63.6%, respectively. The performance indicator for COPD/ asthma ranged from 21.3% to 71.4% for inhaled steroids. Last, for depression, the performance indicator ranged from 7.4% to 66.7% for antidepressant medications. Analysis of variance confirmed that the achievement of guideline-recommended treatment differed significantly across the 203 MA plans (P ≤ 0.02). Table 3 shows that there were significant associations between O/E mortality ratios and the use of guideline-recommended medications across MA plans. In patients with CHF, those plans with higher use of ACE inhibitors/ARBs (r = -0.40; P < 0.0001) and beta-blockers (r = -0.27; P < 0.0001) had significantly lower O/E mortality ratios, and the association with the use of lipid-lowering medications was close to statistical significance (r = -0.11; P = 0.091). Those MA plans with higher composite scores for use of multiple guideline-recommended medications in CHF had significantly lower O/E mortality ratios (r = -0.45; P < 0.0001). The findings for diabetes were similar. Those MA plans with higher composite scores had significantly lower O/E mortality ratios (r = -0.14; P < 0.042). There were no significant associations between performance indicators and mortality in CAD or COPD/asthma. Antidepressant medications showed a paradoxical correlation. Those MA plans with higher use of antidepressant medications had significantly higher O/E mortality ratios (r = 0.28; P < 0.0001).

■■ Discussion
The importance of providing care that is in accord with accepted guidelines is enhanced by the aging of the U.S. population and the corresponding increase in the prevalence of chronic disease. In fact, the relatively new Medicare Part D Star Ratings incorporates medication adherence to guidelines in its scoring of plans and is also linked to reimbursement and bonuses. 24 The challenge is evident in the wide variation that we found in the prescription of guideline-recommended medications. The consequence is revealed by the association of guideline-discordant care with increased mortality among patients with CHF and diabetes. Future efforts should focus on identifying factors that account for plan variation in the prescription of guideline-recommended medications and on remedial quality improvement activities (standing orders, reminder systems, critical care pathways, algorithms, audits). 25,26 The observed variation across MA plans in guidelinerecommended care poses a serious threat to the health and well-being of patients. Mortality is but the most severe consequence of deviation from guidelines that can benefit other outcomes as well. 27 Guideline adherence will become even more critical as the population of older persons with chronic conditions increases. A key component of any solution will be the routine availability of information on quality of care. This study has shown the value of both risk-adjusted mortality rates and Medicare Part D for providing information that can help to focus efforts to improve the quality of health care delivery.
Our findings confirm the expected relationship between the selected performance indicators and patient survival. Plans' adherence to guideline-recommended medications was associated with decreased mortality in CHF. This conforms to literature showing that the use of ACE inhibitors and beta-blockers lowers mortality by 31% and 35% in 1 year, respectively. 28,29 There is, however, conflicting data regarding the mortality benefit of lipid-lowering medications in patients with established CHF, and this corresponds to our finding of equivocal benefit. 30 Our findings also suggest that there are greater effects with multiple coexisting performance indicators in conditions such as CHF and diabetes. The summation of the coexisting guideline-recommended medications was associated with better survival.
There was a paradoxical relationship between the use of antidepressants and mortality. A steadily increasing body of literature continues to document an association between depression and mortality in elderly populations. 31,32 We cannot state that higher use of antidepressant medications was the cause of higher mortality across plans. It may be that the use of antidepressants is a marker for worse depression. Severe depression has been associated with decreased self-care, 33,34,35,36 which may not be completely addressed by treatment. 37 We found no significant association between variation in the prescription of guideline-recommended medications and mortality in patients with CAD and COPD/asthma. This may be because the treatment effect is small in COPD or because it is tempered by other aspects of care in CAD, such as coronary bypass grafting. In addition, we measured all-cause mortality, so detrimental effects of suboptimal treatment for our study conditions may have been counterbalanced by good treatment for other conditions. Last, we may have needed a larger number of patients and/or longer follow-up to detect the effect of guideline-recommended medications on mortality. 38

■■ Conclusions
In summary, elderly patients are a fragile group with higher mortality when not treated according to clinical practice guidelines. Our results are the first to indicate that there is substantial variation across MA plans in the prescription of guidelinerecommended medications and that this variation correlates with differences in patient survival. These findings can serve to both motivate and help target quality improvement programs.

Limitations
There are several limitations of this study. First, we found that 15.2% of the HOS patients did not have Part D Medicare claims. There are several possibilities regarding these patients: (a) they might not be enrolled in the Part D program, (b) they might be enrolled but not using medications, (c) they might have other prescription coverage (e.g., a company's retirement insurance), and (d) they might have missing information. Second, the Medicare Part D claim file does not contain drug class assignment. We compared our drug class algorithm with the HEDIS 2008 NDC class assignment. We found that both algorithms produced similar results (data not shown). Third, we did not have disease severity measures. However, we used measures of illness burden such as the PCS and MCS in our analysis. Fourth, we were unable to incorporate contraindications, drug interaction, and allergy. Those patients for whom the drug may be contraindicated were not excluded from the denominator. A more accurate count of patients receiving the desired drug could be made if patient allergies were to become listed in the Medicare Part D file. Fifth, deviation from guideline-recommended prescriptions might be due to specific situations, which are unique to elderly patients, such as presence of multiple conditions and polypharmacy. 39 Older patients with comorbidities might require multiple medication use, which may ameliorate symptoms and improve or preserve quality of life. Unfortunately, multiple medication use is also a major risk factor for prescribing and adherence problems, adverse drug events, and other adverse health outcomes. Sixth, we have not taken into account the number of guidelines satisfied across conditions (e.g., patients who have diabetes plus CHF) due to sample-size limitations. Seventh, we did not examine the effect of the "doughnut hole" on clinical outcomes. Among Medicare Part D enrollees in 2007 who were not eligible for the lowincome subsidies, 26% had spending high enough to reach the coverage gap. 40 "Between 2007 and 2017, the dollar value of the coverage gap is projected to double, exposing some beneficiaries to potentially high out-of-pocket costs and increasing the risk of cost-related noncompliance." 41 Eighth, assumptions made about individuals based on aggregate data are vulnerable to the ecological fallacy. This does not mean that identifying associations between aggregate figures is necessarily defective, and it doesn't necessarily mean that any inferences drawn about associations between the characteristics of an aggregate population and the characteristics of subunits within the population are absolutely wrong either. What it does say is that the process of aggregating or disaggregating data may conceal the variations that are not visible at the larger aggregate level. Last, the use of propensity scores for balancing the medication groups controls the confounding by indication; however, this method does not eliminate the problems of endogeneity.